arXiv subjects

Qiang Yin

Publications and source records attributed to Qiang Yin.

17 recordsLinked to original sources

arXiv2026

DBcover: A White-box SQL Test Generation Framework for Coverage Improvement

Relational Database Management Systems (RDBMSs) are the backbone of modern data-intensive applications, making reliability and robustness critical. However, achieving high coverage in RDBMS testing remains challenging because of large codebases and complex execution logic. Traditional fuzzing relies on random SQL generation and cannot capture the correspondence between SQL inputs and internal execution paths, while symbolic execution suffers from prohibitive cost and scalability limitations. We propose DBcover, an LLM-driven white-box SQL test generation framework based on contextual reasoning. DBcover uses lightweight dynamic analysis to extract SQL-to-path correspondence and call graphs as global context, and collects source-level information around target functions as local context. These contexts are organized in a unified knowledge graph for efficient retrieval and reuse. DBcover then performs two-phase test generation: it first selects a semantically relevant seed whose execution path is close to the uncovered target, and then guides the LLM with global and local context to generate SQL test cases that trigger previously uncovered code regions. Experiments show that DBcover achieves 80.1% and 82.3% coverage on PostgreSQL and MySQL, and is also effective on the enterprise RDBMS KingbaseES, demonstrating its practical applicability to closed-source systems.

cs.DB↗

arXiv2026

PriSAR: 3D Geometric-Prior-Guided Diffusion for Parameter-Controlled SAR Image Generation

Synthetic aperture radar (SAR) image generation can mitigate data scarcity, but controllablegeneration under sparse observation angles remains difficult. Recent SAR generative studies im-prove texture realism, yet explicit geometry-aware control is still limited. This paper studiesthe focused and verifiable setting of intermediate-azimuth completion: 3D-model-derived geo-metric priors guide a diffusion model to synthesize the views missing from sparse-angle trainingdata. GeoDiff-SAR constructs a lightweight multi-bounce ray-tracing prior, encodes the result-ing point cloud, and fuses it with text conditioning while adapting Stable Diffusion 3.5 Mediumthrough low-rank adaptation. On a real four-category aircraft dataset, GeoDiff-SAR reaches anSSIM of 0.812 and azimuth consistency of 0.940, compared with 0.738 and 0.782 for the text-conditioned SD3.5 Medium baseline. The same sparse-angle protocol on five MSTAR vehicleclasses yields an SSIM of 0.878 and azimuth consistency of 0.917. These results support theconclusion that a lightweight 3D geometric prior improves viewpoint adherence for controllableSAR generation; it is intended as generation guidance rather than high-fidelity electromagneticreconstruction.

eess.IV↗

arXiv2026

Physics-Aware Complex-Valued State Space Model with Scattering-Prior Feature Modulation for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are closely coupled with electromagnetic scattering mechanisms. Many existing complex-valued networks can preserve amplitude-phase information, but they are often limited in long-range spatial dependency modeling and usually incorporate polarimetric priors only as input-level or shallow auxiliary features. As a result, physical knowledge is insufficiently used to guide deep feature evolution. To address this issue, this paper proposes CV-SSMNet, a physics-aware complex-valued state-space network with scattering-aware feature modulation for PolSAR image classification. The proposed method builds a complex-valued state-space model (CV-SSM) in the original complex domain to capture long-range spatial dependencies while preserving polarimetric amplitude-phase coupling. Meanwhile, seven physically meaningful scattering priors, are encoded as FiLM-style modulation signals to adaptively recalibrate complex-valued representations during feature evolution. CV-SSMNet further integrates multi-scale complex convolutions, branch-wise CV-SSM encoding, prior-guided recalibration, and lightweight global context aggregation, enabling physically guided representation learning from local scattering structures to global spatial context. Experiments on three L-band benchmark datasets and an additional P-band BIOMASS evaluation demonstrate that CV-SSMNet achieves competitive accuracy, improved regional consistency, and better boundary preservation, supporting the effectiveness of embedding polarimetric scattering mechanisms into complex-valued long-range GeoAI representation learning.

cs.CV↗

arXiv2026

Cross-Sensor SAR Data Generation Using Diffusion Models and Feature Migration

Different synthetic aperture radar (SAR) sensors vary significantly in resolution, polarization modes, and frequency bands, making it difficult to directly apply existing models to newly launched SAR satellites. These new systems require large amounts of labeled data for model retraining, but collecting sufficient data in a short time is often infeasible. To address this contradiction, this paper proposes a data generation and transfer framework, integrating a stable diffusion model with attention distillation, that leverages historical SAR data to synthesize training data tailored to the unique characteristics of new SAR systems. Specifically, we fine-tune the low-rank adaptation (LoRA) modules within the multimodal diffusion transformer (MM-DiT) architecture to enable class-controllable SAR image generation guided by textual prompts. To ensure that the generated images reflect the statistical properties and imaging characteristics of the target SAR system, we further introduce an attention distillation mechanism that transfers sensor-specific features, such as spatial texture, speckle distribution, and structural patterns, from real target-domain data to the generative model. Extensive experiments on multi-class aircraft target datasets from two real spaceborne SAR systems demonstrate the effectiveness of the proposed approach in alleviating data scarcity and supporting cross-sensor remote sensing applications.

eess.IV↗

arXiv2026

Optical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning

Few-shot class-incremental learning (FSCIL) in synthetic aperture radar imagery presents unique challenges due to severe data scarcity and SAR-specific variability. In particular, strong azimuth sensitivity in SAR induces large intra-class variation and inter-class confusion, and FSCIL sequential updates further lead to catastrophic forgetting of previously learned classes. Inspired by neural collapse, we propose an optical-guided SAR FSCIL framework, which derives orthogonal feature subspaces from a data-rich optical ATR dataset and uses them as geometric priors to guide SAR feature learning. SAR features are projected onto these orthogonal subspaces via principal angle constraints, effectively transferring discriminative structure from the optical to the SAR domain. Specifically, our projection loss and the classifier loss optimized with a frozen simplex-ETF geometry jointly induce neural collapse by concentrating features around class means while maintaining large inter-class angles. We evaluate the approach on a benchmark comprising an optical ATR dataset and a SAR ATR dataset with 24 target classes, organized into a base training session and seven incremental sessions. Compared with recent FSCIL methods including NCFSCIL and so on, our method achieves the highest final accuracy and a favorable trade-off between final performance and performance degradation. Moreover, neural collapse metrics show improved intra-class compactness and inter-class separability, indicating that the learned features more closely approximate the ideal simplex-ETF geometry.

cs.CV↗

arXiv2026

GeoDiff-SAR II: 3D-Driven Foundation Diffusion Models for SAR Generation via Decoupled Control

Existing Synthetic Aperture Radar (SAR) image generation methods still lack reliable controllability over key imaging parameters, particularly azimuth angle, depression angle, and polarization mode. Our preliminary GeoDiff-SAR supported limited azimuth completion, but remained ineffective for large missing azimuth sectors and did not provide unified control over multiple imaging conditions. To address this problem, we propose GeoDiff-SAR II, a 3D model-guided decoupled framework for controllable SAR image generation. The proposed framework imposes controllability through physically grounded geometric-electromagnetic cues rather than image intensity alone. We introduce a Geometric-Electromagnetic Conditioning Map (GECM), a structured intermediate representation that encodes the target pose map and dominant scattering centers, thereby decoupling macroscopic geometry from microscopic scattering responses. During training, GECMs are derived from real sparse-azimuth SAR images. During inference, the same representation is rendered directly from a 3D CAD model under specified azimuth, depression angle, and polarization conditions, enabling physically consistent control across large viewpoint gaps. The imaging parameters are further converted into text conditions, while the GECM is injected through ControlNet to provide explicit spatial guidance. Combined with Low-Rank Adaptation (LoRA) on a FLUX backbone, the proposed framework unifies geometric-electromagnetic conditioning and parameter-aware generation within a single process. Experiments on simulated and real datasets demonstrate controllable generation over key SAR imaging parameters, stable generalization across large azimuth gaps, and consistent improvements in image fidelity, physical consistency, and downstream Automatic Target Recognition (ATR) performance.

eess.IV↗

arXiv2026

GeoDiff-SAR: A Geometric Prior Guided Diffusion Model for SAR Image Generation

eess.IV↗

arXiv2025

SMGeo: Cross-View Object Geo-Localization with Grid-Level Mixture-of-Experts

Cross-view object Geo-localization aims to precisely pinpoint the same object across large-scale satellite imagery based on drone images. Due to significant differences in viewpoint and scale, coupled with complex background interference, traditional multi-stage "retrieval-matching" pipelines are prone to cumulative errors. To address this, we present SMGeo, a promptable end-to-end transformer-based model for object Geo-localization. This model supports click prompting and can output object Geo-localization in real time when prompted to allow for interactive use. The model employs a fully transformer-based architecture, utilizing a Swin-Transformer for joint feature encoding of both drone and satellite imagery and an anchor-free transformer detection head for coordinate regression. In order to better capture both inter-modal and intra-view dependencies, we introduce a grid-level sparse Mixture-of-Experts (GMoE) into the cross-view encoder, allowing it to adaptively activate specialized experts according to the content, scale and source of each grid. We also employ an anchor-free detection head for coordinate regression, directly predicting object locations via heat-map supervision in the reference images. This approach avoids scale bias and matching complexity introduced by predefined anchor boxes. On the drone-to-satellite task, SMGeo achieves leading performance in accuracy at IoU=0.25 and mIoU metrics (e.g., 87.51%, 62.50%, and 61.45% in the test set, respectively), significantly outperforming representative methods such as DetGeo (61.97%, 57.66%, and 54.05%, respectively). Ablation studies demonstrate complementary gains from shared encoding, query-guided fusion, and grid-level sparse mixture-of-experts.

cs.CV↗

arXiv2025

Reconsidering the calculation of the false vacuum decay rate at zero temperature

In the calculation of the decay rate at finite temperature using the saddle point approximation, we identified some inconsistencies in the calculation of the decay rate at zero temperature. These inconsistencies may impact the explanation provided by Callan and Coleman. To address these inconsistencies, we recalculated the decay rate using the shifted-bounce solution and the shot solution.

hep-th↗

arXiv2024

Saddle-point approximation to the false vacuum decay at finite temperature in one-dimensional quantum mechanics

We calculate the false-vacuum decay rate in one-dimensional quantum mechanics on the basis of the saddle-point approximation in the Euclidean path integral at finite temperature. The saddle points are the finite-T and shifted bounce solutions, which are finite-period analogs of the (zero-temperature) bounce solution, and the shot solutions. We re-examined the zero-temperature result by Callan and Coleman and compare with the zero-temperature limit of our results. We also perform some numerical calculations to illustrate the temperature dependence of the decay rate and compare it with the result by Affleck.

hep-th↗

arXiv2023

Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph

Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, incremental graph computation can reduce unnecessary recomputation. However, a small change may propagate over the whole graph and lead to large-scale iterative computations. To address this problem, we propose Layph, a two-layered graph framework. The upper layer is a skeleton of the graph, which is much smaller than the original graph, and the lower layer has some disjointed subgraphs. Layph limits costly global iterative computations on the original graph to the small graph skeleton and a few subgraphs updated with the input graph changes. In this way, many vertices and edges are not involved in iterative computations, significantly reducing the communication overhead and improving incremental graph processing performance. Our experimental results show that Layph outperforms current state-of-the-art incremental graph systems by 9.08X on average (up to 36.66X) in response time.

cs.DC↗

arXiv2017

SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks

The outstanding pattern recognition performance of deep learning brings new vitality to the synthetic aperture radar (SAR) automatic target recognition (ATR). However, there is a limitation in current deep learning based ATR solution that each learning process only handle one SAR image, namely learning the static scattering information, while missing the space-varying information. It is obvious that multi-aspect joint recognition introduced space-varying scattering information should improve the classification accuracy and robustness. In this paper, a novel multi-aspect-aware method is proposed to achieve this idea through the bidirectional Long Short-Term Memory (LSTM) recurrent neural networks based space-varying scattering information learning. Specifically, we first select different aspect images to generate the multi-aspect space-varying image sequences. Then, the Gabor filter and three-patch local binary pattern (TPLBP) are progressively implemented to extract a comprehensive spatial features, followed by dimensionality reduction with the Multi-layer Perceptron (MLP) network. Finally, we design a bidirectional LSTM recurrent neural network to learn the multi-aspect features with further integrating the softmax classifier to achieve target recognition. Experimental results demonstrate that the proposed method can achieve 99.9% accuracy for 10-class recognition. Besides, its anti-noise and anti-confusion performance are also better than the conventional deep learning based methods.

cs.CV↗

arXiv2017

A GPU Based Memory Optimized Parallel Method For FFT Implementation

FFT (fast Fourier transform) plays a very important role in many fields, such as digital signal processing, digital image processing and so on. However, in application, FFT becomes a factor of affecting the processing efficiency, especially in remote sensing, which large amounts of data need to be processed with FFT. So shortening the FFT computation time is particularly important. GPU (Graphics Processing Unit) has been used in many common areas and its acceleration effect is very obvious compared with CPU (Central Processing Unit) platform. In this paper, we present a new parallel method to execute FFT on GPU. Based on GPU storage system and hardware processing pipeline, we improve the way of data storage. We divided the data into parts reasonably according the size of data to make full use of the characteristics of the GPU. We propose the memory optimized method based on share memory and texture memory to reduce the number of global memory access to achieve better efficiency. The results show that the GPU-based memory optimized FFT implementation not only can increase over 100% than FFTW library in CPU platform, but also can improve over 30% than CUFFT library in GPU platform.

cs.DC↗

arXiv2017

Two Lower Bounds for BPA

Branching bisimilarity on normed Basic Process Algebra (BPA) was claimed to be EXPTIME-hard in previous papers without any explicit proof. Recently it is reminded by Jančar that the claim is not so dependable. In this paper, we develop a new complete proof for EXPTIME-hardness of branching bisimilarity on normed BPA. We also prove the associate regularity problem on normed BPA is PSPACE-hard and in EXPTIME. This improves previous P-hard and NEXPTIME result.

cs.LO↗

arXiv2015

On the Computation Power of Name Parameterization in Higher-order Processes

Parameterization extends higher-order processes with the capability of abstraction (akin to that in lambda-calculus), and is known to be able to enhance the expressiveness. This paper focuses on the parameterization of names, i.e. a construct that maps a name to a process, in the higher-order setting. We provide two results concerning its computation capacity. First, name parameterization brings up a complete model, in the sense that it can express an elementary interactive model with built-in recursive functions. Second, we compare name parameterization with the well-known pi-calculus, and provide two encodings between them.

cs.LO↗

arXiv2014

Branching Bisimilarity Checking for PRS

Recent studies reveal that branching bisimilarity is decidable for both nBPP (normed Basic Parallel Process) and nBPA (normed Basic Process Algebra). These results lead to the question if there are any other models in the hierarchy of PRS (Process Rewrite System) whose branching bisimilarity is decidable. It is shown in this paper that the branching bisimilarity for both nOCN (normed One Counter Net) and nPA (normed Process Algebra) is undecidable. These results essentially imply that the question has a negative answer.

cs.LO↗

arXiv2014

Dividing Line between Decidable PDA's and Undecidable Ones

Senizergues has proved that language equivalence is decidable for disjoint epsilon-deterministic PDA. Stirling has showed that strong bisimilarity is decidable for PDA. On the negative side Srba demonstrated that the weak bisimilarity is undecidable for normed PDA. Later Jancar and Srba established the undecidability of the weak bisimilarity for disjoint epsilon-pushing PDA and disjoint epsilon-popping PDA. These decidability and undecidability results are extended in the present paper. The extension is accomplished by looking at the equivalence checking issue for the branching bisimilarity of several variants of PDA.

cs.LO↗